An investigation into mechanical properties of the nanocomposite with aligned CNT by means of electrical conductivity
Source abstract
Abstract
Abstract In the present study, a novel modelling approach based on electrical properties was proposed to replicate the mechanical behaviour of aligned carbon nanotube/polymer nanocomposites. Firstly, an electrical analytical model with Monte-Carlo method involved was established and validated by accurately predicted electric conductivity. The microstructure of the nanocomposite was then determined according to the electric property. Subsequently, a large-scale representative volume element model based on the predicted distribution of the carbon nanotubes was built to replicate the mechanical response of the nanocomposite under tension, which can be validated by existing experiments. To consider the crystalline structure of the matrix, two cases on the nanocomposites with crystalline and amorphous polymer were investigated, locating their difference on the bonding condition of the interface between CNT and matrix. Results evidenced that the electrical properties of nanocomposites can be used to identify the internal microstructure of nanocomposite. Moreover, the effects of the loading direction, the interfacial strength and the weight fraction were studied by numerical models. The reinforcement effect of the carbon nanotubes was significant when loaded along the aligned direction, but the effect was limited in the other directions. The modulus and the strength of nanocomposite were improved by the increase of the weight fraction of CNTs, while the increase of interfacial strength improves the strength of nanocomposite along CNT-aligned direction significantly, but had negligible effect on its modulus.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Carbon Nanotubes in Composites · Conducting polymers and applications · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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